請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83253
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 王慧瑜 | zh_TW |
dc.contributor.advisor | Hui-Yu Wang | en |
dc.contributor.author | 羅可容 | zh_TW |
dc.contributor.author | Ko-Jung Lo | en |
dc.date.accessioned | 2023-02-01T17:05:33Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-02-01 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-12-21 | - |
dc.identifier.citation | 中文資料
洪明生(2020年3月19日)。邊境管制衝擊外籍漁工!台灣漁業人力受影響。民視。新聞網址:https://www.ftvnews.com.tw/news/detail/2020319U02M1 翁筱郡 、郭南榮 、何宗儒 、陳俊德(2005)。衛星遙測海面溫度應用於黃鰭鮪漁場變動之研究。航測及遙測學刊,第十卷,第二期,139-158 頁。 財團法人中華民國對外漁業合作發展協會。VMS系統簡介。網址:https://www.ofdc.org.tw:8181/web/app/display.xhtml?id=6 財團法人中華民國對外漁業合作發展協會。2017 至 2019 年大型遠洋鮪延繩釣漁業漁獲統計年報。網址:https://www.ofdc.org.tw:8181/web/components/Editor/webs/files/2017-2019TunaAnnualReport.pdf 財團法人中華民國對外漁業合作發展協會。2018至2020年大型遠洋鮪延繩釣漁業漁獲統計年報。網址:https://www.ofdc.org.tw:8181/web/components/Editor/webs/files/2018-2020TunaAnnualReport.pdf 陳文深(民國97年)。100噸以上漁船赴印度洋從事捕撈鮪旗魚類作業應行遵守及注意事項簡介。農政與農情,190期。網址:https://www.coa.gov.tw/ws.php?id=17511&RWD_mode=N 陳彥廷(2020年7月24日)。日本疫情不退消費減 冷凍鮪類銷日船塞港。自由時報。新聞網址:https://news.ltn.com.tw/news/life/breakingnews/3239107 陳璋玲、陳文深 (民國89年)。我國遠洋漁船安置監控系統之相關規定。農政與農情,96期。網址:https://www.coa.gov.tw/ws.php?id=2383 經濟部,台北駐日經濟文化代表處經濟組(2022 年 4 月 12 日)。新冠疫情、原油價格高漲及日圓重貶之3重影響,臺灣鮪釣漁船出海恐再減少,日本冷凍大目鮪市場行情「已無降價空間」。新聞網址:https://www.trade.gov.tw/Pages/Detail.aspx?nodeid=45&pid=740827 西文資料 Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., & Boer, M. M. (2021). Connections of climate change and variability to large and extreme forest fires in Southeast Australia. Communications Earth & Environment, 2 (1). https://doi.org/10.1038/s43247-020-00065-8 Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716–723. https://doi.org/10.1109/tac.1974.1100705 Arrizabalaga, H., Dufour, F., Kell, L., Merino, G., Ibaibarriaga, L., Chust, G., Irigoien, X., Santiago, J., Murua, H., Fraile, I., Chifflet, M., Goikoetxea, N., Sagarminaga, Y., Aumont, O., Bopp, L., Herrera, M., Marc Fromentin, J., & Bonhomeau, S. (2015). Global habitat preferences of commercially valuable tuna. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 102–112. https://doi.org/10.1016/j.dsr2.2014.07.001 Barange, M., Bahri, T., Beveridge, M. C. M., Cochrane, K. L., Funge-Smith, S., & Poulain, F. (2018). Impacts of climate change on fisheries and aquaculture: Synthesis of currrent knowledge, adaptation and mitigation options. FAO Fisheries and Aquaculture Technical Paper, 627. https://agris.fao.org/agris-search/search.do?recordID=XF2018002008 Chang, Y.J., Hsu, J., Lai, P.K., Lan, K.W., Tsai, W.P. (2021). Evaluation of the impacts of climate change on albacore distribution in the South Pacific Ocean by using ensemble forecast. Frontiers in Marine Science, 8, 731950. Chen, I.-C., Lee, P.-F., Tzeng, W.-N. (2005). Distribution of albacore (Thunnus alalunga) in the Indian Ocean and its relation to environmental factors. Fisheries Oceanography. 14, 71-80. Cheung, William & Lam, Vicky & Sarmiento, Jorge & Kearney, Kelly & Watson, Reg & Zeller, Dirk & Pauly, Daniel. (2010). Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16 (1), 24–35. https://doi.org/10.1111/j.1365-2486.2009.01995.x Cheung, W. W., Watson, R., & Pauly, D. (2013). Signature of ocean warming in global fisheries catch. Nature, 497 (7449), 365–368. https://doi.org/10.1038/nature12156 Clavelle, T. (2021, July 8). Covid-19 brings unmatched downturn in global fishing activity. Global Fishing Watch. Retrieved July 3, 2022, from https://globalfishingwatch.org/data/covid-19-brings-unmatched-downturn-in-global-fishing-activity/ Conan, S. M.-H. & Brummer, G. J. A. (2000). Fluxes of planktic foraminifera in response to monsoonal upwelling on the Somalia Basin margin. Deep Sea Research Part II: Topical Studies in Oceanography, 47 (9-11), 2207–2227. https://doi.org/10.1016/S0967-0645(00)00022-9 Coro, G., Ellenbroek, A., & Pagano, P. (2021). An open science approach to infer fishing activity pressure on stocks and biodiversity from Vessel Tracking Data. Ecological Informatics, 64, 101384. https://doi.org/10.1016/j.ecoinf.2021.101384 Crespo, G. O., Dunn, D. C., Reygondeau, G., Boerder, K., Worm, B., Cheung, W., Tittensor, D. P., & Halpin, P. N. (2018). The environmental niche of the global high seas pelagic longline fleet. Science Advances, 4 (8). https://doi.org/10.1126/sciadv.aat3681 Cutlip, K. (2016). Teaching machines to tell us about fishing. Global Fishing Watch. Retrieved July 17, 2022, from https://globalfishingwatch.org/data/teaching-machines-to-tell-us-about-fishing/ de Souza, E. N., Boerder, K., Matwin, S., & Worm, B. (2016). Improving fishing pattern detection from satellite AIS using data mining and machine learning. PLOS ONE, 11 (7). https://doi.org/10.1371/journal.pone.0158248 Eastwood, P. D., Mills, C. M., Aldridge, J. N., Houghton, C. A., & Rogers, S. I. (2007). Human activities in UK offshore waters: An assessment of direct, physical pressure on the seabed. ICES Journal of Marine Science, 64 (3), 453–463. https://doi.org/10.1093/icesjms/fsm001 Erauskin‐Extramiana, M., Arrizabalaga, H., Hobday, A. J., Cabré, A., Ibaibarriaga, L., Arregui, I., Murua, H., & Chust, G. (2019). Large‐scale distribution of tuna species in a warming ocean. Global Change Biology, 25 (6), 2043–2060. https://doi.org/10.1111/gcb.14630 Food and Agriculture Organization. (1999). Technical papers presented at the Regional Workshop on Fisheries Monitoring, Control and Surveillance - Kuala Lumpur and Kuala Terengganu, Malaysia, 29 June - 3 July 1998 - Supplement 2 Technical Papers. Chapter 2 – Legal aspects of MCS. Rome. https://www.fao.org/3/X1353E/x1353e01.pdf Food and Agriculture Organization. (2001). International Plan of Action to Prevent Deter and Eliminate Illegal, Unreported and Unregulated Fishing. Rome. https://www.fao.org/3/y1224e/Y1224E.pdf Food and Agriculture Organization. (2019). Regulation of the Minister of Transportation of the R.I. No. PM 7 of 2019 on the Installation and Activation of Automatic Identification Systems for Ships Sailing in the Indonesian Waters. FAOLEX Database. https://www.fao.org/faolex/results/details/en/c/LEX-FAOC193419/ Froese, R. & D. Pauly. Editors. (2022). FishBase. World Wide Web electronic publication.www.fishbase.org, version (06/2022). Gerritsen, H. & Lordan, C. (2010). Integrating Vessel Monitoring Systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution. ICES Journal of Marine Science, 68 (1), 245–252. https://doi.org/10.1093/icesjms/fsq137 Grüss, A., Chagaris, D. D., Babcock, E. A., & Tarnecki, J. H. (2018). Assisting ecosystem-based fisheries management efforts using a comprehensive survey database, a large environmental database, and generalized additive models. Marine and Coastal Fisheries, 10 (1), 40–70. https://doi.org/10.1002/mcf2.10002 Hanamoto, E. (1987). Effect of oceanographic environment on bigeye tuna distribution. Fisheries Oceanography. 51, 203–216. Hastie, T. J. & Tibshirani, R. J. (1990). Generalized additive models. Routledge. https://doi.org/10.1201/9780203753781 He, B., Yan, F., Yu, H., Su, F., Lyne, V., Cui, Y., Kang, L., et al. (2021). Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020. Remote Sensing, 13 (22), 4507. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs13224507 Hilborn, R., Amoroso, R. O., Anderson, C. M., Baum, J. K., Branch, T. A., Costello, C., de Moor, C. L., Faraj, A., Hively, D., Jensen, O. P., Kurota, H., Little, L. R., Mace, P., McClanahan, T., Melnychuk, M. C., Minto, C., Osio, G. C., Parma, A. M., Pons, M., ...Ye, Y. (2020). Effective fisheries management instrumental in improving fish stock status. Proceedings of the National Academy of Sciences, 117 (4), 2218–2224. https://doi.org/10.1073/pnas.1909726116 Hsieh, C.-hao, Reiss, C. S., Hunter, J. R., Beddington, J. R., May, R. M., & Sugihara, G. (2006). Fishing elevates variability in the abundance of exploited species. Nature, 443 (7113), 859–862. https://doi.org/10.1038/nature05232 Huang, H.-W. (2011). Bycatch of high sea longline fisheries and measures taken by Taiwan: Actions and challenges. Marine Policy, 35 (5), 712–720. https://doi.org/10.1016/j.marpol.2011.02.012 Intergovernmental Panel on Climate Change, IPCC. (2021). Fact sheets. Climate Change 2021: The Physical Science Basis. Retrieved June 27, 2022. https://www.ipcc.ch/report/ar6/wg1/resources/factsheets/ IOTC. (2017a). Supporting information for Albacore, updated: December 2016. Status summary for species of tuna and tuna-like species under the IOTC mandate, as well as other species impacted by IOTC fisheries. Retrieved July 21, 2022. https://www.iotc.org/sites/default/files/documents/science/species_summaries/english/Albacore_Supporting_information.pdf IOTC. (2017b). Supporting information for Bigeye tuna, updated: December 2017. Status summary for species of tuna and tuna-like species under the IOTC mandate, as well as other species impacted by IOTC fisheries. Retrieved July 21, 2022. https://www.iotc.org/sites/default/files/documents/science/species_summaries/english/Bigeye_tuna_Supporting_information.pdf IOTC. (2017c). Supporting information for Yellowfin tuna, updated: July 2021. Status summary for species of tuna and tuna-like species under the IOTC mandate, as well as other species impacted by IOTC fisheries. Retrieved July 21, 2022. https://www.iotc.org/sites/default/files/content/Stock_status/YFT_supporting_information_July2021.pdf Isaksen, J. R., Hermansen, Ø., & Flaaten, O. (2015). Stubborn fuel tax concessions: the case of fisheries in Norway. Marine Policy, 52, 85-92. James, M., Mendo, T., Jones, E. L., Orr, K., McKnight, A., & Thompson, J. (2018). AIS data to inform small scale fisheries management and Marine Spatial Planning. Marine Policy, 91, 113–121. https://doi.org/10.1016/j.marpol.2018.02.012 Joo, R., Salcedo, O., Gutierrez, M., Fablet, R., & Bertrand, S. (2015). Defining fishing spatial strategies from VMS data: Insights from the world's largest monospecific fishery. Fisheries Research, 164, 223–230. https://doi.org/10.1016/j.fishres.2014.12.004 Kim, Kwang-il & Lee, Keon Myung (2020). Convolutional neural network-based gear type identification from automatic identification system trajectory data. Applied Sciences, 10 (11), 4010. https://doi.org/10.3390/app10114010 Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., Wilson, A., Bergman, B., White, T. D., Block, B. A., Woods, P., Sullivan, B., Costello, C., & Worm, B. (2018). Tracking the global footprint of fisheries. Science, 359 (6378), 904–908. https://doi.org/10.1126/science.aao5646 Kumar, P. S., Pillai, G. N., & Manjusha, U. (2014). El Nino Southern Oscillation (ENSO) impact on tuna fisheries in Indian Ocean. SpringerPlus, 3 (1). https://doi.org/10.1186/2193-1801-3-591 Lan, K.-W., Kawamura, H., Lee, M.-A., Lu, H.-J., Shimada, T., Hosoda, K., & Sakaida, F. (2012a). Relationship between albacore (thunnus alalunga) fishing grounds in the Indian Ocean and the thermal environment revealed by cloud-free microwave sea surface temperature. Fisheries Research, 113 (1), 1–7. https://doi.org/10.1016/j.fishres.2011.08.017 Lan, K.-W., Evans, K., & Lee, M.-A. (2012b). Effects of climate variability on the distribution and fishing conditions of Yellowfin Tuna (Thunnus albacares) in the western Indian Ocean. Climatic Change, 119 (1), 63–77. https://doi.org/10.1007/s10584-012-0637-8 Lee, H. H. & Hsu, C. C. (2008). Abundance index for longline fishery targeting spawning Pacific bluefin tunathunnus orientalisin south-western North Pacific Ocean. Fisheries Science, 74 (6), 1336–1338. https://doi.org/10.1111/j.1444-2906.2008.01661.x Lee, P. F., Chen, I. C., and Tzeng, W. N. (2005). Spatial and Temporal distributions patterns of bigeye tuna (Thunnusobsesus) in the Indian Ocean, Zoological Studies, 44, 260–270. Le Tixerant, M., Le Guyader, D., Gourmelon, F., & Queffelec, B. (2018). How can automatic identification system (AIS) data be used for maritime spatial planning? Ocean & Coastal Management, 166, 18–30. https://doi.org/10.1016/j.ocecoaman.2018.05.005 Lehodey, P., Bertignac, M., Hampton, J., Lewis, A., & Picaut, J. (1997). El Niño Southern Oscillation and tuna in the Western Pacific. Nature, 389 (6652), 715–718. https://doi.org/10.1038/39575 Lehodey, P., Senina, I., Nicol, S., & Hampton, J. (2015). Modelling the impact of climate change on South Pacific Albacore Tuna. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 246–259. https://doi.org/10.1016/j.dsr2.2014.10.028 Li, M. L., Ota, Y., Underwood, P. J., Reygondeau, G., Seto, K., Lam, V. W., Kroodsma, D., & Cheung, W. W. (2021). Tracking industrial fishing activities in African waters from space. Fish and Fisheries. https://doi.org/10.1111/faf.12555 Liu, T.-K., Chen, Y.-S., & Chen, Y.-T. (2019). Utilization of Vessel Automatic Identification System (AIS)to estimate the emission of air pollutant from merchant vessels in the port of Kaohsiung. Aerosol and Air Quality Research, 19 (10), 2341–2351. https://doi.org/10.4209/aaqr.2019.07.0355 Lévy, M., Shankar, D., André, J.-M., Shenoi, S. S., Durand, F., & de Boyer Montégut, C. (2007). Basin-wide seasonal evolution of the Indian Ocean's phytoplankton blooms. Journal of Geophysical Research, 112 (C12). https://doi.org/10.1029/2007jc004090 Majkowski, J. (2007). Review of the state of world marine fishery resources. FAO Fisheries Technical Paper No.438. Rome. Marzuki, M. I., Gaspar, P., Garello, R., Kerbaol, V., & Fablet, R (2017). Fishing gear identification from vessel-monitoring-system-based fishing vessel trajectories. IEEE Journal of Oceanic Engineering, 43 (3), 689–699. https://doi.org/10.1109/joe.2017.2723278 McCauley, D. J., Woods, P., Sullivan, B., Bergman, B., Jablonicky, C., Roan, A., Hirshfield, M., Boerder, K., & Worm, B. (2016). Ending hide and seek at sea. Science, 351 (6278), 1148–1150. https://doi.org/10.1126/science.aad5686 Michael, P. E., Wilcox, C., Tuck, G. N., Hobday, A. J., & Strutton, P. G. (2017). Japanese and Taiwanese pelagic longline fleet dynamics and the impacts of climate change in the southern Indian Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 140, 242–250. https://doi.org/10.1016/j.dsr2.2016.12.003 Mohri, M. & Nishida, T. (1999). Seasonal changes in bigeye tuna fishing areas in relation to the oceanographic parameters in the Indian Ocean. IOTC Proceedings no. 2, 221-230. Murase, H., Nagashima, H., Yonezaki, S., Matsukura, R., & Kitakado, T. (2009). Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: A case study in Sendai Bay, Japan. ICES Journal of Marine Science, 66 (6), 1417–1424. https://doi.org/10.1093/icesjms/fsp105 Murawski, S. A., Wigley, S. E., Fogarty, M. J., Rago, P. J., & Mountain, D. G. (2015). Effort distribution and catch patterns adjacent to temperate mpas. ICES Journal of Marine Science, 62 (6), 1150–1167. https://doi.org/10.1016/j.icesjms.2005.04.005 Natale, F., Gibin, M., Alessandrini, A., Vespe, M., & Paulrud, A. (2015). Mapping fishing effort through AIS Data. PLOS ONE, 10 (6). https://doi.org/10.1371/journal.pone.0130746 Nurdiati, S., Bukhari, F., Julianto, M. T., Sopaheluwakan, A., Aprilia, M., Fajar, I., Septiawan, P., & Najib, M. K. (2022). The impact of El Niño Southern Oscillation and Indian Ocean dipole on the burned area in Indonesia. Terrestrial, Atmospheric and Oceanic Sciences, 33 (1). https://doi.org/10.1007/s44195-022-00016-0 Nurholis, Sukoraharjo, S. S., Lumban-Gaol, J., & Syah, A. F. (2021). Yellowfin tuna fishing dynamic during low iod positive and negative at Eastern Indian Ocean; study case at Western Waters of sumatera. IOP Conference Series: Earth and Environmental Science, 695 (1), 012010. https://doi.org/10.1088/1755-1315/695/1/012010 Orben, R. A., Adams, J., Hester, M., Shaffer, S. A., Suryan, R. M., Deguchi, T., Ozaki, K., Sato, F., Young, L. C., Clatterbuck, C., Conners, M. G., Kroodsma, D. A., & Torres, L. G. (2021). Across borders: External factors and prior behaviour influence North Pacific Albatross Associations with fishing vessels. Journal of Applied Ecology, 58 (6), 1272–1283. https://doi.org/10.1111/1365-2664.13849 O’Shea, E. J. (2016). Changes in habitat preference of tuna species and implication for regional fisheries management: Southern bluefin tuna fishing in the Indian Ocean. Australian Journal of Maritime & Ocean Affairs, 8 (2), 117–131. https://doi.org/10.1080/18366503.2016.1191789 Pallotta, G., Vespe, M., & Bryan, K. (2013). Vessel pattern knowledge discovery from AIS DATA: A framework for anomaly detection and route prediction. Entropy, 15 (12), 2218–2245. https://doi.org/10.3390/e15062218 Pauly, D. & Christensen, V. (1995). Primary production required to sustain global fisheries. Nature, 374 (6519), 255–257. https://doi.org/10.1038/374255a0 Posen, P. E., Lee, J., Large, P. A., & Kenny, A. J. (2014). Using vessel monitoring system (VMS) data to assess the impact of marine protection boundaries on Blue Ling fishing northwest of the British Isles. Aquatic Living Resources, 27 (1), 17–26. https://doi.org/10.1051/alr/2014001 Rijnsdorp, A. (1998). Micro-scale distribution of beam trawl effort in the southern North Sea between 1993 and 1996 in relation to the trawling frequency of the sea bed and the impact on benthic organisms. ICES Journal of Marine Science, 55 (3), 403–419. https://doi.org/10.1006/jmsc.1997.0326 Roxy, M. K., Gnanaseelan, C., Parekh, A., Chowdary, J. S., Singh, S., Modi, A., Kakatkar, R., Mohapatra, S., Dhara, C., Shenoi, S. C., & Rajeevan, M. (2020). Indian ocean warming. Assessment of Climate Change over the Indian Region, 191–206. https://doi.org/10.1007/978-981-15-4327-2_10 Sagarminaga, Y. & Arrizabalaga, H. (2014). Relationship of northeast Atlantic Albacore juveniles with surface thermal and chlorophyll-a fronts. Deep Sea Research Part II: Topical Studies in Oceanography, 107, 54–63. https://doi.org/10.1016/j.dsr2.2013.11.006 Saji, N. H., Goswami, B. N., Vinayachandran, P. N., & Yamagata, T. (1999). A dipole mode in the tropical Indian Ocean. Nature, 401 (6751), 360–363. https://doi.org/10.1038/43854 Salas, S. & Gaertner, D (2004). The Behavioural Dynamics of Fishers: Management Implications. Fish and Fisheries, 5 (2), 153–167. https://doi.org/10.1111/j.1467-2979.2004.00146.x Salas, S., Sumaila, U. R., & Pitcher, T. (2004). Short-term decisions of small-scale fishers selecting alternative target species: A choice model. Canadian Journal of Fisheries and Aquatic Sciences, 61 (3), 374–383. https://doi.org/10.1139/f04-007 Schott, F. A. & McCreary, J. P. (2001). The monsoon circulation of the Indian Ocean. Progress in Oceanography, 51 (1), 1–123. https://doi.org/10.1016/s0079-6611 (01)00083-0 Skaar, K. L., Jørgensen, T., Ulvestad, B. K., & Engås, A. (2011). Accuracy of VMS data from Norwegian demersal stern trawlers for estimating trawled areas in the Barents Sea. ICES Journal of Marine Science, 68 (8), 1615–1620. https://doi.org/10.1093/icesjms/fsr091 Small, L. F. & Menzies, D. W. (1981). Patterns of primary productivity and biomass in a coastal upwelling region. Deep Sea Research Part A. Oceanographic Research Papers, 28 (2), 123–149. https://doi.org/10.1016/0198-0149(81)90086-8 Song, L., Zhou, J., Zhou, Y., Nishida, T., Jiang, W., & Wang, J. (2009). Environmental preferences of bigeye tuna, thunnus obesus, in the Indian Ocean: An application to a longline fishery. Environmental Biology of Fishes, 85 (2), 153–171. https://doi.org/10.1007/s10641-009-9474-7 Song, L. & Zhou, Y. (2010). Developing an integrated habitat index for Bigeye Tuna (Thunnus obesus) in the Indian Ocean based on Longline Fisheries Data. Fisheries Research, 105 (2), 63–74. https://doi.org/10.1016/j.fishres.2010.03.004 Soykan, C. U., Eguchi, T., Kohin, S., & Dewar, H. (2014). Prediction of fishing effort distributions using boosted regression trees. Ecological Applications, 24 (1), 71–83. https://doi.org/10.1890/12-0826.1 Sreeush, M. G., Valsala, V., Pentakota, S., Prasad, K. V., & Murtugudde, R. (2018). Biological production in the Indian Ocean Upwelling zones –part 1: Refined estimation via the use of a variable compensation depth in ocean carbon models. Biogeosciences, 15 (7), 1895–1918. https://doi.org/10.5194/bg-15-1895-2018 Syed, S., Borit, M., & Spruit, M. (2018). Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016. Fish and Fisheries, 19 (4), 643–661. https://doi.org/10.1111/faf.12280 Taconet, M., Fernandes, J. A., Miller, N. A., Kroodsma, D. (2019). Conclusions and overview of Global Atlas of AIS-based fishing activity. In Taconet, M., Kroodsma, D., Fernandes, J.A. (eds.) Global Atlas of AIS-based fishing activity - Challenges and opportunities. Rome, FAO. (also available at www.fao.org/3/ca7012en/ca7012en.pdf Torres-Irineo, E., Salas, S., Euán-Ávila, J. I., Palomo, L. E., Quijano Quiñones, D. R., Coronado, E., & Joo, R. (2021). Spatio-temporal determination of small-scale vessels’ fishing grounds using a vessel monitoring system in the southeastern Gulf of Mexico. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.643318 Tsai, Wen-Pei; Sun, Chi-Lu; Liu, Kwang-Ming; Wang, Shyh-Bin; and Lo, Nancy C. H. (2015). CPUE standardization and catch estimate of the blue shark by Taiwanese large-scale tuna longline fishery in the North Pacific Ocean. Journal of Marine Science and Technology: Vol. 23 : Iss. 4 , Article 21. DOI: 10.6119/JMST-014-1230-1 United Nations, UN Statistics Division, AIS Handbook, Overview of AIS dataset. Available at https://unstats.un.org/wiki/display/AIS/Overview+of+AIS+dataset Vedernikov, V. I., Gagarin, V. I., Demidov, A. B., Burenkov, V. I., & Stunzhas, P. A. (2007). Primary production and chlorophyll distributions in the subtropical and tropical waters of the Atlantic Ocean in the autumn of 2002. Oceanology, 47 (3), 386–399. https://doi.org/10.1134/s0001437007030113 Ward, P. & Hindmarsh, S. (2007). An overview of historical changes in the fishing gear and practices of pelagic longliners, with particular reference to Japan’s Pacific Fleet. Reviews in Fish Biology and Fisheries, 17 (4), 501–516. https://doi.org/10.1007/s11160-007-9051-0 White, T. D., Ferretti, F., Kroodsma, D. A., Hazen, E. L., Carlisle, A. B., Scales, K. L., Bograd, S. J., & Block, B. A. (2019). Predicted hotspots of overlap between highly migratory fishes and industrial fishing fleets in the Northeast Pacific. Science Advances, 5 (3). https://doi.org/10.1126/sciadv.aau3761 Wiggert, J. D., Jones, B. H., Dickey, T. D., Brink, K. H., Weller, R. A., Marra, J., & Codispoti, L. A. (2000). The northeast monsoon's impact on mixing, phytoplankton biomass and nutrient cycling in the Arabian Sea. Deep Sea Research Part II: Topical Studies in Oceanography, 47 (7-8), 1353–1385. https://doi.org/10.1016/s0967-0645 (99)00147-2 Wilen, James. E. (2004). Spatial management of fisheries. Marine Resource Economics, 19, 7–19. Worm, B. & Tittensor, D. P. (2011). Range contraction in large pelagic predators. Proceedings of the National Academy of Sciences, 108 (29), 11942–11947. https://doi.org/10.1073/pnas.1102353108 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83253 | - |
dc.description.abstract | 鮪漁業為全球最重要的漁業之一,由於氣候變遷可能導致鮪魚的分布、資源量改變,其漁業資源的管理必須考慮氣候變遷的影響機制。然而,過往研究偏向於環境變化對資源量與生物特性的影響,針對漁船作業時空分布與環境因子互動的研究較為缺乏。本研究針對印度洋熱帶鮪類的重要漁場進行大尺度分析,使用 2016至2019年間漁船船舶自動識別系統(Automatic Identification System)數據,探討台灣籍延繩釣漁船時空分布與表水溫、葉綠素a(chlorophyll-a)、印度洋偶極指數(Dipole Model Index)的相關性。
研究發現,延繩釣漁船作業集中在印度洋西北及西南海域,並具有明顯季節性趨勢。本研究依據漁船目標魚種偏好棲息的區域,以南緯15°為界,分南北兩區討論。結果顯示影響捕撈時數的環境因子中,以表水溫的關聯性最高,在南緯15°以北的海域,表水溫與捕撈時數呈線性相關,作業時數在表水溫大於28.8°C的海域增加。南區捕撈時數與表水溫呈非線性相關,表水溫在12.2°C至20.7°C間的海域對捕撈時數有正向影響。在氣候變遷,海洋暖化的情境下,表水溫可以做為評估未來漁撈動態變化的參考。 | zh_TW |
dc.description.abstract | Tunas sustain some of the world's most commercially valuable fisheries. Since climate change may largely affect the distribution and abundance of tuna species, management of fishery resources must take these mechanisms into account. However, previous studies have focused on the effects of environmental changes on resource abundance and biological characteristics; studies on the interactions between vessel activities and environmental factors are limited. In this study, we investigated Taiwanese longline fishing vessels' spatial-temporal distribution and the relationship between sea surface temperature, chlorophyll-a, and Dipole Model Index.
We found that vessel operations were concentrated in the northwestern and southwestern of the Indian Ocean, with clear seasonal patterns. The study area was divided into north and south regions based on the boundary of 15°S latitude by the temperature preference of the target species. The results showed that sea surface temperature had the highest correlation among the environmental factors. In the northern region, sea surface temperature was linearly correlated with fishing hours, and the hours increased when the temperature was more significant than 28.8°C. In the southern region, fishing hours were non-linearly correlated with sea surface temperature; a positive effect on fishing hours occurred between 12.2°C and 20.7°C. The findings suggest that sea surface temperature can be used as a reference to assess future fishing dynamics. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-02-01T17:05:33Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-02-01T17:05:33Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii 英文摘要 iii 第一章 緒論 1 1.1 研究動機 1 1.2 研究背景 1 1.3 印度洋之海洋環境 2 1.4 海表溫與初級生產力對大洋洄游性魚類漁海況之影響 3 1.5 漁船軌跡數據在漁業科學上的應用 4 1.6 研究目的 6 第二章 資料與方法 7 2.1 研究時間及地點 7 2.2 資料來源 8 2.3 研究方法 10 第三章 研究結果 12 第四章 結論與建議 15 參考資料 19 圖 32 附錄 40 | - |
dc.language.iso | zh_TW | - |
dc.title | 海洋環境因子影響鮪釣船捕撈時數 : 印度洋大尺度空間分析 | zh_TW |
dc.title | The Impact of Oceanic Environments on Fishing Hours of Tuna Longline Vessels: A Large Scale Spatial-temporal Analysis in the Indian Ocean | en |
dc.title.alternative | The Impact of Oceanic Environments on Fishing Hours of Tuna Longline Vessels: A Large Scale Spatial-temporal Analysis in the Indian Ocean | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 羅敏輝;曾琬鈴 | zh_TW |
dc.contributor.oralexamcommittee | Min-Hui Lo;Wan-Ling Tseng | en |
dc.subject.keyword | 印度洋,鮪魚,海洋表水溫,船舶自動識別系統, | zh_TW |
dc.subject.keyword | Indian Ocean,Tuna,Sea surface temperature,Automatic Identification System, | en |
dc.relation.page | 43 | - |
dc.identifier.doi | 10.6342/NTU202210124 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2022-12-23 | - |
dc.contributor.author-college | 理學院 | - |
dc.contributor.author-dept | 氣候變遷與永續發展國際學位學程 | - |
顯示於系所單位: | 生物科技研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-0155221211167007.pdf 目前未授權公開取用 | 4.57 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。